digitado

For years, passwords were treated like the front door of the internet. Everything depended on them. Your email. Your bank account. Your cloud storage. Your company dashboard. Your social media. Your identity. One secret string of characters stood between you and everyone who should not be there. That system was always flawed. It survived not because it was great, but because it was simple, familiar, and cheap. Passwords were easy to create, easy to deploy, and easy for […]

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Identifying abnormal behaviors plays a vital role in ensuring cloud infrastructure remains secure and operationally stable. Conventional methods that aggregate data at a single location create substantial privacy concerns, especially within shared cloud platforms hosting multiple organizations with confidential operational information. This paper proposes HierFedDP, a hierarchical federated learning framework integrated with a two-stage differential privacy mechanism for privacy-preserving anomaly detection in cloud services. Our approach employs a three-tier architecture consisting of local clients, edge servers, and a […]

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Autonomous tracked amphibious robotic systems operating across water and land environments are essential for coastal inspection, disaster response, environmental monitoring, and complex terrain exploration. However, discontinuous water-land dynamics, unstable medium switching, and safety-critical control under environmental uncertainty pose significant challenges to existing amphibious navigation and path planning methods, where global reachability and adaptive decision-making are difficult to unify. Motivated by these challenges, this paper proposes CD-HSSRL, a Cross-Domain Hierarchical Safe-Switching Reinforcement Learning framework for autonomous tracked amphibious navigation. […]

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Path planning is critical for multi-robot systems (MRS), directly affecting task efficiency, execution time and operational cost. Despite extensive research and the successful application of numerous algorithms, achieving globally optimal solutions in cluttered or dynamic environments remains a significant challenge. Issues such as scalability with in-creasing numbers of robots, computational efficiency, system robustness, and coordination complexity continue to drive the development of more reliable approaches. This study reviews modelling approaches, optimisation criteria, and solution algorithms based on roadmap […]

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Everyone has a version of the same fear: “AI is moving faster than I can keep up. What happens when my hard-earned skills stop mattering?” That fear isn’t irrational. But it’s also misdiagnosed. AI anxiety isn’t primarily a reaction to model capability. It’s a reaction to losing control — of learning, of work, of identity. The real shift isn’t “machines got smart.” It’s that the economy is re-pricing execution. So, if you’re trying to survive this wave by […]

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Ordinary differential equations (ODEs) provide a powerful framework for modeling dynamic systems arising in a wide range of scientific domains. However, most existing ODE methods focus on a single system, and do not adequately address the problem of learning shared patterns from multiple heterogeneous dynamic systems. In this article, we propose a novel distributionally robust learning approach for modeling heterogeneous ODE systems. Specifically, we construct a robust dynamic system by maximizing a worst-case reward over an uncertainty class […]

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Role-playing games (RPGs) rely on character systems as the primary interface between narrative, player agency, and gameplay mechanics. Unlike static visual assets, RPG characters must preserve identity across customization, animation, equipment variation, and long-term progression. This paper presents a comprehensive review of character design and animation pipelines in RPG-based games, integrating perspectives from visual identity construction, motion synthesis, and AI-assisted generation workflows. The review synthesizes foundational studies on shape language, avatar representation, and expressive animation with recent advancements […]

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Landauer’s principle gives a lower bound on the amount of energy it takes to erase one bit of information: E ≥ log(2) kB T where kB is the Boltzmann constant and T is the ambient temperature in Kelvin. The lower bound applies no matter how the bit is physically stored. There is no theoretical lower limit on the energy required to carry out a reversible calculation. In practice the energy required to erase a bit is around a […]

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This paper addresses complex decision-making scenarios characterized by high uncertainty and high-cost errors, researching a risk-sensitive decision-oriented reinforcement learning mining method. It focuses on resolving the reliability issues arising from tail instability in the reward distribution and out-of-distribution actions under offline data conditions. Methodologically, the decision-making process is modeled using a Markov framework, with the reward distribution as the learning object to retain value information under adverse conditions. Based on this, a conditional risk-value metric is introduced to […]

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Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane — from order timestamps and routing records to shipping manifests — yet predicting delivery delays remains predominantly reactive. Existing predictive approaches typically treat this problem either as a tabular classification task, ignoring network topology, or as a time-series anomaly detection task, overlooking the spatial dependencies of the supply chain graph. To bridge this gap, we propose a hybrid deep learning framework for proactive […]

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